Enhanced swarm intelligence optimization: Inspired by cellular coordination in immune systems

Abstract

Swarm intelligence optimization algorithms (SIOAs) are widely used to address complex problems but often trapped in local optima. To overcome this, we propose a novel multi-population SIOA based on cellular coordination mechanisms in immune systems (referred as CCOA), inspired by the immune system's efficient elimination of viruses. The CCOA consists of four key units: an intra-population cell communication unit, an inter-population cell communication unit, a cell migration unit, and a cell division unit. Through collaborative communication, the intra-population and inter-population units guide cells towards global optima, enhancing the algorithm's global search ability. The cell migration unit improves convergence speed by guiding cell movements. The cell division unit allows cells to divide in large numbers during the late iterations for improved convergence accuracy. We evaluate the effectiveness of the CCOA using eight standard test functions and apply it to river flow prediction in an Elman neural network (referred as CCOA-Elman). The results obtained from optimizing test functions demonstrate that the CCOA outperforms existing algorithms in accuracy, convergence, and stability. The real-world application illustrates that the CCOA-Elman achieves over 95% prediction accuracy, surpassing the accuracies of three other models. The CCOA offers a promising approach to overcome local optima in optimization.